

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__lowercase     : List[str]       =						logging.get_logger(__name__)


__lowercase     : Optional[int]       =						{
    '''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
    '''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
    '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
    '''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}



class 						_A     (				snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      List[Any]					=   '''codegen'''
  __lowerCamelCase			:      List[str]					=   {
      '''max_position_embeddings''': '''n_positions''',
      '''hidden_size''': '''n_embd''',
      '''num_attention_heads''': '''n_head''',
      '''num_hidden_layers''': '''n_layer''',
  }





  def __init__(  self   ,SCREAMING_SNAKE_CASE_=50400   ,SCREAMING_SNAKE_CASE_=2048   ,SCREAMING_SNAKE_CASE_=2048   ,SCREAMING_SNAKE_CASE_=4096   ,SCREAMING_SNAKE_CASE_=28   ,SCREAMING_SNAKE_CASE_=16   ,SCREAMING_SNAKE_CASE_=64   ,SCREAMING_SNAKE_CASE_=None   ,SCREAMING_SNAKE_CASE_="gelu_new"   ,SCREAMING_SNAKE_CASE_=0.0   ,SCREAMING_SNAKE_CASE_=0.0   ,SCREAMING_SNAKE_CASE_=0.0   ,SCREAMING_SNAKE_CASE_=1E-5   ,SCREAMING_SNAKE_CASE_=0.02   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=50256   ,SCREAMING_SNAKE_CASE_=50256   ,SCREAMING_SNAKE_CASE_=False   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      snake_case							:		Dict        =		vocab_size
      snake_case							:		List[str]        =		n_ctx
      snake_case							:		List[Any]        =		n_positions
      snake_case							:		Dict        =		n_embd
      snake_case							:		Union[str, Any]        =		n_layer
      snake_case							:		Tuple        =		n_head
      snake_case							:		List[str]        =		n_inner
      snake_case							:		Union[str, Any]        =		rotary_dim
      snake_case							:		Optional[Any]        =		activation_function
      snake_case							:		List[Any]        =		resid_pdrop
      snake_case							:		Tuple        =		embd_pdrop
      snake_case							:		int        =		attn_pdrop
      snake_case							:		str        =		layer_norm_epsilon
      snake_case							:		Union[str, Any]        =		initializer_range
      snake_case							:		Dict        =		use_cache

      snake_case							:		int        =		bos_token_id
      snake_case							:		str        =		eos_token_id

      super().__init__(
          bos_token_id=SCREAMING_SNAKE_CASE_   ,eos_token_id=SCREAMING_SNAKE_CASE_   ,tie_word_embeddings=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    )







class 						_A     (				snake_case				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = "default"   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = False   ,):

      '''simple docstring'''
      super().__init__(SCREAMING_SNAKE_CASE_   ,task=SCREAMING_SNAKE_CASE_   ,patching_specs=SCREAMING_SNAKE_CASE_   ,use_past=SCREAMING_SNAKE_CASE_    )
      if not getattr(self._config   ,"""pad_token_id"""   ,SCREAMING_SNAKE_CASE_    ):
          # TODO: how to do that better?
          snake_case							:		Optional[Any]        =		0





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		int        =		OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}    )
      if self.use_past:
          self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_   ,direction="""inputs"""    )
          snake_case							:		int        =		{0: """batch""", 1: """past_sequence + sequence"""}
      else:
          snake_case							:		Union[str, Any]        =		{0: """batch""", 1: """sequence"""}

      return common_inputs





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      return self._config.n_layer





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      return self._config.n_head





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = -1   ,SCREAMING_SNAKE_CASE_ = -1   ,SCREAMING_SNAKE_CASE_ = False   ,SCREAMING_SNAKE_CASE_ = None   ,):

      '''simple docstring'''
      snake_case							:		Tuple        =		super(SCREAMING_SNAKE_CASE_   ,self    ).generate_dummy_inputs(
          SCREAMING_SNAKE_CASE_   ,batch_size=SCREAMING_SNAKE_CASE_   ,seq_length=SCREAMING_SNAKE_CASE_   ,is_pair=SCREAMING_SNAKE_CASE_   ,framework=SCREAMING_SNAKE_CASE_    )

      # We need to order the input in the way they appears in the forward()
      snake_case							:		List[Any]        =		OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}    )

      # Need to add the past_keys
      if self.use_past:
          if not is_torch_available():
              raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""    )
          else:
              import torch

              snake_case      ,		snake_case							:		Optional[Any]        =		common_inputs["""input_ids"""].shape
              # Not using the same length for past_key_values
              snake_case							:		str        =		seqlen + 2
              snake_case							:		Optional[Any]        =		(
                  batch,
                  self.num_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // self.num_attention_heads,
              )
              snake_case							:		Any        =		[
                  (torch.zeros(SCREAMING_SNAKE_CASE_    ), torch.zeros(SCREAMING_SNAKE_CASE_    )) for _ in range(self.num_layers    )
              ]

      snake_case							:		List[str]        =		common_inputs["""attention_mask"""]
      if self.use_past:
          snake_case							:		Tuple        =		ordered_inputs["""attention_mask"""].dtype
          snake_case							:		Union[str, Any]        =		torch.cat(
              [ordered_inputs["""attention_mask"""], torch.ones(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,dtype=SCREAMING_SNAKE_CASE_    )]   ,dim=1    )

      return ordered_inputs





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      return 13









'''simple docstring'''


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class  __lowercase    (	__magic_name__							,				unittest.TestCase			):
   _a    						=   PriorTransformer
   _a    						=   """hidden_states"""







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       4
     __a								=       8
     __a								=       7

     __a								=       floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       floats_tensor((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=0   )					->     Union[str, Any]:
     torch.manual_seed(UpperCamelCase   )
     __a								=       4
     __a								=       8
     __a								=       7

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   @property
   def 		UpperCamelCase__      (						self   )					->     List[str]:
     return (4, 8)







   @property
   def 		UpperCamelCase__      (						self   )					->     str:
     return (4, 8)







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       {
         'num_attention_heads': 2,
         'attention_head_dim': 4,
         'num_layers': 2,
         'embedding_dim': 8,
         'num_embeddings': 7,
         'additional_embeddings': 4,
     }
     __a								=       self.dummy_input
     return init_dict, inputs_dict







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a						,							__a								=       PriorTransformer.from_pretrained(
         'hf-internal-testing/prior-dummy'   ,				output_loading_info=UpperCamelCase   )
     self.assertIsNotNone(UpperCamelCase   )
     self.assertEqual(len(loading_info['missing_keys']   )   ,				0   )

     model.to(UpperCamelCase   )
     __a								=       model(**self.dummy_input   )[0]

     assert hidden_states is not None, "Make sure output is not None"







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a						,							__a								=       self.prepare_init_args_and_inputs_for_common()

     __a								=       self.model_class(**UpperCamelCase   )
     __a								=       inspect.signature(model.forward   )
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     __a								=       [*signature.parameters.keys()]

     __a								=       ['hidden_states', 'timestep']
     self.assertListEqual(arg_names[:2]   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'   )
     __a								=       model.to(UpperCamelCase   )

     if hasattr(UpperCamelCase   ,				'set_default_attn_processor'   ):
       model.set_default_attn_processor()

     __a								=       self.get_dummy_seed_input()

     with torch.no_grad():
       __a								=       model(**UpperCamelCase   )[0]

     __a								=       output[0, :5].flatten().cpu()
     print(UpperCamelCase   )

     # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
     # the expected output slices are not the same for CPU and GPU.
     __a								=       torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]   )
     self.assertTrue(torch_all_close(UpperCamelCase   ,				UpperCamelCase   ,				rtol=1e-2   )   )





@slow
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=1   ,				UpperCamelCase=768   ,				UpperCamelCase=77   ,				UpperCamelCase=0   )					->     List[str]:
     torch.manual_seed(UpperCamelCase   )
     __a								=       batch_size
     __a								=       embedding_dim
     __a								=       num_embeddings

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
           [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
           # fmt: on
       ]   )
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'   ,				subfolder='prior'   )
     model.to(UpperCamelCase   )
     __a								=       self.get_dummy_seed_input(seed=UpperCamelCase   )

     with torch.no_grad():
       __a								=       model(**UpperCamelCase   )[0]

     assert list(sample.shape   ) == [1, 768]

     __a								=       sample[0, :8].flatten().cpu()
     print(UpperCamelCase   )
     __a								=       torch.tensor(UpperCamelCase   )

     assert torch_all_close(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-3   )



